Automated Robotics, Blockchain, and AI in Revolutionary Manufacturing System

Read full text
Authors: IJMSRT
Cited by: 0

Abstract

Abstract The core outcome of revolutionary manufacturing, usually known as Industry 4.0, is the development of intelligent factories. These factories leverage advanced robotics, big data, cloud computing, the robust security measures, smart sensors, the Internet of Things (IoT), and other cutting-edge technologies to enhance efficiency, safety, and costeffectiveness. Robotics plays a vital role in Industry 4.0, offering extensive capabilities in manufacturing by improving automation, performing repetitive tasks with precision, and reducing operational costs. Another key technology driving the transformation of manufacturing is Artificial Intelligence powered block chain. In an Industry 4.0 environment, AI and block chain work together to secure the vast amounts of data generated by IoT devices. Blockchain ensures data integrity and immutability, while AI analyzes the data to optimize processes and extract valuable insights. AI algorithms also enhance blockchain security and facilitate the analysis of manufacturing data recorded over the blockchain, enabling predictive maintenance and the quality control. This proactive approach helps detect and resolve potential issues before they lead to defects or downtime. Additionally, smart contracts on the blockchain can autonomously execute predefined tasks, streamlining processes and reducing the need for intermediaries. This paper explores the applications of robotics and AI-powered block chain technology in Industry 4.0, discussing their impact and potential future research directions. These technologies enable automation, support hazardous and complex tasks, withstand extreme temperatures, and operate continuously over extended periods in assembly lines.

Fields of Study & Subjects

Digital Transformation in Industry Impact of AI and Big Data on Business and Society Blockchain Technology Applications and Security

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
29 January 2026